import os
import json
import time
import logging
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import List, Dict, Any, Optional, Callable
from ..utils.config_loader import ConfigLoader
from ..models.test_result import TestResult
from ..models.device_info import DeviceInfo
from ..utils.performance_tracker import PerformanceTracker

class ParallelEngine:
    """并行测试执行引擎，支持多设备并行运行测试用例"""
    
    def __init__(self, config_path: str = None, device_count: int = None):
        """
        初始化并行测试引擎
        
        Args:
            config_path: 配置文件路径
            device_count: 指定设备数量（优先级高于配置文件）
        """
        # 加载配置
        self.config_loader = ConfigLoader(config_path)
        self.config = self.config_loader.get_config()['parallel_engine']
        
        # 设置工作线程数
        if device_count is not None:
            self.max_workers = device_count
        else:
            self.max_workers = self.config['max_workers']
        
        # 性能追踪器
        self.perf_tracker = PerformanceTracker()
        
        # 初始化日志
        logging.basicConfig(
            level=getattr(logging, self.config_loader.get_config()['logging']['level']),
            format=self.config_loader.get_config()['logging']['format']
        )
        self.logger = logging.getLogger('ParallelEngine')
        
        # 设备管理
        self.devices = []
        self.device_tasks = {}
        
        self.logger.info(f"并行引擎初始化完成，最大工作线程数: {self.max_workers}")
    
    def discover_devices(self) -> List[DeviceInfo]:
        """
        发现可用的测试设备（物理设备或模拟器）
        
        Returns:
            设备信息列表
        """
        # TODO: 实现鸿蒙设备发现逻辑
        # 这里模拟返回设备列表
        devices = []
        for i in range(min(self.max_workers, 8)):  # 最大模拟8个设备
            devices.append(DeviceInfo(
                device_id=f"device_{i}",
                device_type="simulator",
                os_version="HarmonyOS 3.0",
                status="available"
            ))
        
        self.devices = devices
        self.logger.info(f"发现 {len(devices)} 个可用设备")
        return devices
    
    def run_test_suite(self, test_suite_path: str) -> Dict[str, Any]:
        """
        运行测试套件
        
        Args:
            test_suite_path: 测试套件路径
            
        Returns:
            测试结果汇总
        """
        start_time = time.time()
        self.logger.info(f"开始执行测试套件: {test_suite_path}")
        
        # 发现设备
        self.discover_devices()
        
        # 加载测试用例
        test_cases = self._load_test_cases(test_suite_path)
        total_tests = len(test_cases)
        self.logger.info(f"加载了 {total_tests} 个测试用例")
        
        # 性能追踪开始
        self.perf_tracker.start()
        
        # 执行测试
        results = self._execute_tests_parallel(test_cases)
        
        # 性能追踪结束
        performance_data = self.perf_tracker.stop()
        
        # 计算总执行时间
        total_time = time.time() - start_time
        
        # 生成结果汇总
        summary = self._generate_summary(results, total_time, performance_data)
        
        self.logger.info(f"测试套件执行完成，总耗时: {total_time:.2f}秒")
        return summary
    
    def _load_test_cases(self, test_suite_path: str) -> List[Dict[str, Any]]:
        """加载测试用例"""
        # TODO: 实现实际的测试用例加载逻辑
        # 这里模拟返回测试用例列表
        test_cases = []
        for i in range(20):  # 模拟20个测试用例
            test_cases.append({
                "id": f"test_{i}",
                "name": f"测试用例 {i}",
                "priority": "high" if i % 3 == 0 else "medium"
            })
        return test_cases
    
    def _execute_tests_parallel(self, test_cases: List[Dict[str, Any]]) -> List[TestResult]:
        """
        并行执行测试用例
        
        Args:
            test_cases: 测试用例列表
            
        Returns:
            测试结果列表
        """
        results = []
        
        # 使用ThreadPoolExecutor进行并行执行
        with ThreadPoolExecutor(max_workers=min(self.max_workers, len(self.devices))) as executor:
            # 提交任务
            future_to_test = {
                executor.submit(self._execute_test, test_case, device): (test_case, device)
                for i, (test_case, device) in enumerate(zip(
                    test_cases,
                    [self.devices[i % len(self.devices)] for i in range(len(test_cases))]
                ))
            }
            
            # 处理完成的任务
            for future in as_completed(future_to_test):
                test_case, device = future_to_test[future]
                try:
                    result = future.result()
                    results.append(result)
                    self.logger.info(f"测试用例 {test_case['name']} 在设备 {device.device_id} 上执行完成，结果: {result.status}")
                except Exception as e:
                    # 记录失败的测试
                    failed_result = TestResult(
                        test_id=test_case['id'],
                        test_name=test_case['name'],
                        device_id=device.device_id,
                        status="failed",
                        execution_time=0,
                        error=str(e)
                    )
                    results.append(failed_result)
                    self.logger.error(f"测试用例 {test_case['name']} 执行失败: {str(e)}")
        
        return results
    
    def _execute_test(self, test_case: Dict[str, Any], device: DeviceInfo) -> TestResult:
        """
        在指定设备上执行单个测试用例
        
        Args:
            test_case: 测试用例信息
            device: 设备信息
            
        Returns:
            测试结果
        """
        # 记录开始时间
        start_time = time.time()
        
        try:
            # TODO: 实现实际的测试执行逻辑
            # 这里模拟测试执行，根据测试用例ID模拟不同的执行时间
            execution_time = 1.5 + (test_case['id'].split('_')[1] % 5) * 0.5
            time.sleep(execution_time)  # 模拟测试执行
            
            # 模拟测试结果（90%成功率）
            import random
            status = "passed" if random.random() < 0.9 else "failed"
            
            # 记录执行时间
            actual_execution_time = time.time() - start_time
            
            return TestResult(
                test_id=test_case['id'],
                test_name=test_case['name'],
                device_id=device.device_id,
                status=status,
                execution_time=actual_execution_time,
                error=None if status == "passed" else "模拟测试失败"
            )
        except Exception as e:
            actual_execution_time = time.time() - start_time
            return TestResult(
                test_id=test_case['id'],
                test_name=test_case['name'],
                device_id=device.device_id,
                status="failed",
                execution_time=actual_execution_time,
                error=str(e)
            )
    
    def _generate_summary(self, results: List[TestResult], total_time: float, performance_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        生成测试结果汇总
        
        Args:
            results: 测试结果列表
            total_time: 总执行时间
            performance_data: 性能数据
            
        Returns:
            汇总结果
        """
        passed = sum(1 for r in results if r.status == "passed")
        failed = sum(1 for r in results if r.status == "failed")
        total = len(results)
        
        # 计算串行执行时间（假设）
        serial_time = sum(r.execution_time for r in results)
        speedup = serial_time / total_time if total_time > 0 else 0
        
        return {
            "total_tests": total,
            "passed_tests": passed,
            "failed_tests": failed,
            "pass_rate": passed / total * 100 if total > 0 else 0,
            "total_execution_time": total_time,
            "average_test_time": sum(r.execution_time for r in results) / total if total > 0 else 0,
            "speedup": speedup,
            "performance_improvement_percentage": (speedup - 1) * 100 if speedup > 1 else 0,
            "device_usage": {
                device.device_id: sum(1 for r in results if r.device_id == device.device_id)
                for device in self.devices
            },
            "performance_data": performance_data,
            "detailed_results": [r.to_dict() for r in results]
        }
    
    def stop(self):
        """停止引擎，清理资源"""
        self.logger.info("停止并行测试引擎，清理资源")
        # TODO: 实现资源清理逻辑
        pass